Understanding shopper behavior in a store has become paramount to retailers, merchandisers, data gatherers and those in the business of operating warehouses. To date, understanding and then later predicting shopper behavior at the point of decision, i.e., the first moment of truth, has been elusive. Also elusive has been knowledge of the paths taken by shoppers in a store. Typically, the only consistent actual data known has been that data gained after a shopper has purchased multiple items and then left the store; i.e., data gathering at the point of sale (POS) or sometime afterwards. Persons of ordinary skill in the art have rightly followed up such purchases with interviews in order to gain further insight, but such interviews still miss the mark for “in the moment” or “real-time” shopper behavior, real-time shopper behavioral perspectives, and for knowing the actual and decisional paths taken by shoppers in a store.
The first moment of truth (FMOT) has been defined as product selection which includes 1) the product considered for selection, 2) the product actually selected, 3) the amount of time that a shopper expends to consider a particular product or products for selection, and 4) a shopper's presumed location in a store in relation to a product location at a shopper's moment of decision.
The amount of time that a shopper expends to consider a particular product or products for selection is generally thought to range from about three to about seven seconds, but varies from shopper to shopper. A person of skill in the art will understand these variances more fully. Regardless of the actual time required, the FMOT includes a shopper's decisional process in a store, at the store shelf, end cap, kiosk, stand alone display, a store within a store, or other means of display known to those of skill in the art.
Merchandisers who sell their wares in retail stores spend billions of dollars per year seeking to understand and influence shoppers' behavior during the FMOT and while shoppers travel along their paths in a store. Up until now, real-time empirical understanding about how shoppers behave and what, if anything, influences shoppers during the First Moment of Truth remains a mystery.
While seeking to understand shopper behavior at the FMOT is important, it is also important to provide a method for such discovery that can be consistently applied, provides a whole picture of a shopper's shopping experience, works well, is reproducible and that is truly a measure of the entirety of a shopper's in-store experience.
The prior art provides that the use of RFID to track the movement of products in a store can be useful. Persons of ordinary skill in the art using RFID on products in retail environments will readily acknowledge that less than 10% and often less than 2% of all products in a retail environment (e.g., grocery store) contain RFID tags. Even if RFID tags are attached to shopping carts, such attachment does not provide useful information about what a shopper selects from store shelves, end-caps, etc., but rather where the shopping cart has traveled within the store. Such an approach therefore provides no shopper product selection insights.
In U.S. Pat. No. 6,659,344, a system is provided that gathers data on the behavior of shoppers in a retail market. Herein, a scanner is attached to a shopping basket and is configured to detect, through RFID, the removal of an item from a shelf whereby such items are also equipped with one or more RFID tags. An obvious limitation to this approach, again, is the limited use of available RFID tags on products within a store. By dependence upon RFID, it is unlikely that much useful shopper behavior data can be gathered from which shopper insights may be gained.
What remains therefore is the need for a method by which actual and/or real-time shopper behavior data may be culled through the ability to track a shopper's location throughout a store in real-time, knowledge of which items a shopper has selected for purchase, knowledge of where in the store a shopper has selected an item for purchase, knowledge of how long the decisional process has been for each product selected by a shopper for purchase. Also important is the use of all such real-time shopper behavior data to enhance computer-aided simulation models. Such inclusion would serve to enhance accuracy of computer simulations that predict shopper behavior.